The increasing demand for clean and reliable water resources, coupled with the growing threat of water pollution, has made real-time water quality (WQ) monitoring and assessment a critical priority in many urban areas. Urban environments encounter substantial challenges in maintaining WQ, driven by factors such as rapid population growth, industrial expansion, and the impacts of climate change. Effective real-time WQ monitoring is essential for safeguarding public health, promoting environmental sustainability, and ensuring adherence to regulatory standards. The rapid advancement of Internet of Things (IoT) sensor technologies and smartphone applications presents an opportunity to develop integrated platforms for real-time WQ assessment. Advances in the IoT provide a transformative solution for WQ monitoring, revolutionizing the way we assess and manage our water resources. Moreover, recent developments in Location-Based Services (LBSs) and Global Navigation Satellite Systems (GNSSs) have significantly enhanced the accessibility and accuracy of location information. With the proliferation of GNSS services, such as GPS, GLONASS, Galileo, and BeiDou, users now have access to a diverse range of location data that are more precise and reliable than ever before. These advancements have made it easier to integrate location information into various applications, from urban planning and disaster management to environmental monitoring and transportation. The availability of multi-GNSS support allows for improved satellite coverage and reduces the potential for signal loss in urban environments or densely built environments. To harness this potential and to enable the seamless integration of the IoT and LBSs for sustainable WQ monitoring, a systematic literature review was conducted to determine past trends and future opportunities. This research aimed to review the limitations of traditional monitoring systems while fostering an understanding of the positioning capabilities of LBSs in environmental monitoring for sustainable urban development. The review highlights both the advancements and challenges in using the IoT and LBSs for real-time WQ monitoring, offering critical insights into the current state of the technology and its potential for future development. There is a pressing need for an integrated, real-time WQ monitoring system that is cost-effective and accessible. Such a system should leverage IoT sensor networks and LBSs to provide continuous monitoring, immediate feedback, and spatially dynamic insights, empowering stakeholders to address WQ issues collaboratively and efficiently.

Vegetation land fragmentation has had numerous negative repercussions on sustainable development around the world. Urban planners are currently avidly investigating vegetation land fragmentation due to its effects on sustainable development. The literature has identified a research gap in the development of Artificial Intelligence [AI]-based models to simulate vegetation land fragmentation in urban contexts with multiple affecting elements. As a result, the primary aim of this research is to create an AI-based simulation framework to simulate vegetation land fragmentation in metropolitan settings. The main objective is to use non-linear analysis to identify the factors that contribute to vegetation land fragmentation. The proposed methodology is applied for Western Province, Sri Lanka. Accessibility growth, initial vegetation large patch size, initial vegetation land fragmentation, initial built-up land fragmentation, initial vegetation shape irregularity, initial vegetation circularity, initial building density, and initial vegetation patch association are the main variables used to frame the model among the 20 variables related to patches, corridors, matrix and other. This study created a feed-forward Artificial Neural Network [ANN] using R statistical software to analyze non-linear interactions and their magnitudes. The study likewise utilized WEKA software to create a Decision Tree [DT] modeling framework to explain the effect of variables. According to the ANN olden algorithm, accessibility growth has the maximum importance level [44] between -50 and 50, while DT reveals accessibility growth as the root of the Level of Vegetation Land Fragmentation [LVLF]. Small, irregular, and dispersed vegetation patches are especially vulnerable to fragmentation. As a result, study contributes detech and managing vegetation land fragmentation patterns in urban environments, while opening up vegetation land fragmentation research topics to AI applications.

This study explores Living Structure, originated by Christopher Alexander and further developed by Bin Jiang, within urban settings, particularly in Sri Lanka. Utilizing building footprints as proxies for human movement, the research employs a quantitative approach to analyze and compare Living Structure patterns across Sri Lankan cities. Results demonstrate the presence of Living Structure characteristics in all case study cities, manifested through fitting power law distributions. Notably, historical cities such as Anuradhapura, Kandy, Jaffna, and Galle exhibit stronger Living Structure rankings compared to the newly developed city of Hambantota. These variations are attributed to historical backgrounds and the pace of urban development, with established morphologies supporting Living Structure patterns in historical cities. Beyond academic inquiry, the study offers practical implications for urban planners and decision-makers by illuminating the dynamics behind thriving cities and addressing identified knowledge gaps. By integrating Living Structure principles into urban planning strategies, cities can evolve sustainably over time. Through comparative analyses and exploration of Living Structure evolution within specific cities, the research contributes valuable insights into urban dynamics, guiding future planning efforts.

Effective management of degraded urban water bodies requires spatially dense, low cost, timely water quality monitoring and decision making system. Yet conventional laboratory monitoring remains too costly and time consuming for most cities. This study develops and validates a Geospatial Artificial Intelligence (GeoAI) framework integrating consumer smartphone multi-color-space imaging with Random Forest regression, SHapley Additive exPlanations (SHAP) explainability, Random Forest Spatial Interpolation (RFSI), and a governance centered Decision Error Analysis for urban lake water quality mapping. The framework is applied at Gangarama Lake, the Southwest Basin of Beira Lake, Colombo, Sri Lanka. Past studies have shown that it is the most severely polluted of the four Beira Lake basins (Water Pollution Index 1.02–3.94; COD up to 373.41 mg/L). Data were collected through direct site visits, with the dataset for this study consisting of smartphone images — multiple images (more than six) captured at each of 15 georeferenced locations. Colour features extracted across five colour spaces from images collected using an iPhone 14 Pro were processed through a five-stage GeoAI pipeline: Pearson screening, feature selection, Multiple Linear Regression (MLR) baseline, Random Forest ensemble regression with 5-fold cross-validated hyperparameter optimisation, and SHAP analysis. The primary applicability evaluation uses a Decision Error Analysis, assessing whether predictions lead managers to the correct threshold-based action rather than Root Mean Square Error (RMSE), which is dimensionally specific and scale-dependent, and thus not transferable across study sites. Across 60 threshold assessments (4 parameters × 15 locations), the framework achieved 97% correct governance decisions, zero missed violations, and two conservative false alarms at borderline locations. Outside the ±10% threshold boundary zone, decision accuracy was 100%. The Random Forest multi-color-space model achieved R ² = 0.84 for turbidity and 0.81 for total phosphorus, outperforming single-space MLR by 8–22 R ² percentage points. RFSI outperformed traditional Inverse Distance Weighted (IDW) and Kriging by 14–35% in Leave-One-Out Cross-Validation (LOOCV) RMSE through incorporation of distance-to-inlet spatial covariates. The framework reduces monitoring cost from conventional laboratory-only analysis to smartphone-based analysis.